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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-1000g
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-500m-1000g_ft_BioS73_1kbpHG19_DHSs_H3K27AC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nucleotide-transformer-500m-1000g_ft_BioS73_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7864
- F1 Score: 0.8674
- Precision: 0.8219
- Recall: 0.9183
- Accuracy: 0.8502
- Auc: 0.9181
- Prc: 0.9081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4538 | 0.1864 | 500 | 0.5156 | 0.8245 | 0.7173 | 0.9693 | 0.7797 | 0.9012 | 0.8953 |
| 0.4088 | 0.3727 | 1000 | 0.3942 | 0.8517 | 0.8082 | 0.9001 | 0.8327 | 0.9133 | 0.9102 |
| 0.4018 | 0.5591 | 1500 | 0.3782 | 0.8518 | 0.8159 | 0.8911 | 0.8345 | 0.9127 | 0.9086 |
| 0.4043 | 0.7454 | 2000 | 0.3631 | 0.8599 | 0.7934 | 0.9385 | 0.8367 | 0.9176 | 0.9111 |
| 0.3866 | 0.9318 | 2500 | 0.4011 | 0.8586 | 0.7878 | 0.9434 | 0.8341 | 0.9161 | 0.9099 |
| 0.332 | 1.1182 | 3000 | 0.4966 | 0.8603 | 0.8286 | 0.8946 | 0.8449 | 0.9211 | 0.9181 |
| 0.2948 | 1.3045 | 3500 | 0.4844 | 0.8288 | 0.8643 | 0.7961 | 0.8245 | 0.9155 | 0.9026 |
| 0.3062 | 1.4909 | 4000 | 0.4114 | 0.8449 | 0.8675 | 0.8233 | 0.8386 | 0.9223 | 0.9170 |
| 0.2935 | 1.6772 | 4500 | 0.5448 | 0.8767 | 0.8346 | 0.9232 | 0.8613 | 0.9209 | 0.9102 |
| 0.3113 | 1.8636 | 5000 | 0.4740 | 0.8561 | 0.8329 | 0.8806 | 0.8420 | 0.9200 | 0.9152 |
| 0.2362 | 2.0499 | 5500 | 0.8302 | 0.8514 | 0.8544 | 0.8485 | 0.8420 | 0.9222 | 0.9178 |
| 0.1752 | 2.2363 | 6000 | 0.8359 | 0.8681 | 0.8419 | 0.8959 | 0.8546 | 0.9189 | 0.9049 |
| 0.1585 | 2.4227 | 6500 | 0.6381 | 0.8630 | 0.8150 | 0.9169 | 0.8446 | 0.9141 | 0.9058 |
| 0.1535 | 2.6090 | 7000 | 0.7864 | 0.8674 | 0.8219 | 0.9183 | 0.8502 | 0.9181 | 0.9081 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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